July - AMMJ

Maintenance Decision Support Project
At A Chemical Plant
Ron Jenkins of Orica Australia,
Murray Wiseman and Daming Lin of OMDEC Inc Canada
Abstract - Does condition monitoring deliver the results you expect. Can we sharpen the saw and make a more
informed reliability decision? This project investigated the use of a Maintenance Decision Support tool and how it
may be used to improve reliability decisions based upon failure prediction. Data collection and manipulation proved
to be the single most challenging issue. The accuracy with which failures are reported in the CMMS and the need to
understand which failure modes actually occurred and whether they really failed or were suspended was shown to be
of prime importance if reliability analysis was to succeed. The effort needed by the Reliability Engineer in performing
reliability analysis pales in comparison to that required for the cleansing of the data and for its transformation into
analyzable form. Once good data emerges from the anarchy of styles used within the CMMS, software makes light
of the task of detailed reliability analysis that will enable good maintenance decisions.
INTRODUCTION
This paper provides an insight into the challenges faced by the Reliability Engineer before he can exploit Maintenance
Decision Support software. The intent of this study is to apply such a tool (EXAKT© CBM Decision Optimization www.
omdec.com/wiki) to critical magnetic pumps at the Orica Laverton North Chloralkali Plant in Australia. Conditioning
Monitoring (CM) already existed. Nevertheless unexpected failures have occurred and the need to validate and
improve on the CM process was paramount. Reliability based decisions may be assisted with specific types of data
relating to equipment operation and maintenance. However, it is important to recognize that large volumes of CM data
are no guarantee of good condition based maintenance decision models unless that data reflects the deterioration of
failure modes that actually occur. How do we know what condition monitoring variables are significant? This project
will attempt to use a software tool that analyses CMMS failure data in conjunction with condition monitoring data in
order to identify those monitored variables that influence the probability of occurrence of the targeted failure modes.
The methodology applies a Proportional Hazard Model (PHM)(see Ref 8) to determine not only which monitored
variables are significant but also the precise probabilistic relationship between those variables and equipment failure.
The main objective of this study is to understand the nature
of the data required for this. The paper will discuss a data Tag
Pump Description
acquisition, cleansing and transformation philosophy for P12111A Catholyte Pump A
condition monitoring programs that supports practical
P12111B Catholyte Pump A
decision making in maintenance.
P13005 Caustic Evaporator Feed Pump
SCOPE
The study was limited to four pump sets over two years,
an admittedly small sample. These pumps are all Iwaki
magnetic pumps with Toshiba induction motors on caustic
service as detailed in Table 1 opposite.
RELIABILITY PREDICTION MODELS
There are many reliability prediction software tools on the
market. A basic search on the web reveals a number of
vendors [1], [2], [3] for example. This project aims to trial
one such program, EXAKT© because it is one of the few
that confronts the challenge of achieving verifiable dayto-day
decisions based upon the two principal available
maintenance data sources: the CBM database(s) and the
CMMS database.
Reliability prediction is not new. One of the most widely
recognised models was developed by Weibull in 1951
[4]. He developed a failure analysis method that provided
reliability predictions as well as the level of confidence with
which those predictions may be applied.
P13006 Intermediate Caustic Pump
P12111AM Catholyte Pump A Motor
P12111BM Catholyte Pump B Motor
P13005M Caustic Evaporator Feed Pump Motor
P13006M Intermediate Caustic Pump Motor
Table 1
EXAKT trial sample set
Weibull Distribution - Three of its forms
Weibull also showed that the shape parameter in his equation (above) relating reliability to age provides an indication
of likely failure behavior. For a shape parameter of